NIR Reflection Augmentation for DeepLearning-Based NIR Face Recognition

Abstract

Face recognition using a near-infrared (NIR) sensor is widely applied to practical applications such as mobile unlocking or access control. However, unlike RGB sensors, few deep learning approaches have studied NIR face recognition. We conducted comparative experiments for the application of deep learning to NIR face recognition. To accomplish this, we gathered five public databases and trained two deep learning architectures. In our experiments, we found that simple architecture could have a competitive performance on the NIR face databases that are mostly composed of frontal face images. Furthermore, we propose a data augmentation method to train the architectures to improve recognition of users who wear glasses. With this augmented training set, the recognition rate for users who wear glasses increased by up to 16%. This result implies that the recognition of those who wear glasses can be overcome using this simple method without constructing an additional training set. Furthermore, the model that uses augmented data has symmetry with those trained with real glasses-wearing data regarding the recognition of people who wear glasses.

A DCNN-Based Fast NIR Face Recognition System Robust to Reflected Light From Eyeglasses

“…Despite the high reported accuracy of the existing NIR FR methods [9]- [11], [58], these results did not consider mixed positive pairs. Peng et al [11] excluded NIR face images with eyeglasses in the training and test processes of FR, and Zhang et al [10] utilized the PolyU-NIRFD database [47] as training and test databases to conduct performance eval-uation; as shown in Table 1, there are few mixed positive pairs in the PolyU-NIRFD database.…”

“…To prevent degradation of the NIR FR performance due to reflected light, Jo and Kim [58] added the simple reflected light patterns, such as rectangle, circle, or ellipse shapes, to the parts of the NIR face images near the eyes. Although their data augmentation method improved the NIR FR performance, this approach did not generate the sufficiently realistic reflected light patterns in the NIR face images.…”

“…After reviewing the existing methods [32]- [35], [58], we postulated that there could be a performance improvement in NIR FR in G-NG positive FR scenarios when G2NG data augmentation was well conducted using GAN. In this work, since G2NG data augmentation can be represented as an unpaired image-to-image translation problem, we utilized CycleGAN [44] to generate synthetic images.…”

“…We compared the proposed system with the existing DCNNbased NIR FR methods [9]- [11], [58]. For this experiment, we reproduced the Zhang's method [10] and the Peng's method [11] known to have the NIR FR accuracies of around 98%.…”

“…Therefore, we verified that the implementations of [10] and [11] were correct. The work of Kim's method [9] and Jo's method [58] was also reproduced. Kim's method [9] achieved an identification rate of over 99%.…”